When a photographer captures an image, the image is only a sample of the environment surrounding the photographer. The entire surrounding environment influences the captured image, due to factors such as shading, reflection, sun position, etc. Illumination within an image plays a critical role in the appearance of a scene. Recovering image lighting conditions is important for several digital image editing tasks including, but not limited to, image reconstruction, virtually rendering objects into an image, and digitally altering an image. In particular, when compositing objects into a digital image, an understanding of the scene lighting conditions is important to ensure that the composite is illuminated appropriately so that the composite looks realistic. In addition, scene reconstruction and modeling often requires an estimate of the lighting conditions to produce accurate geometry.
The problem of recovering image lighting conditions is an ill-posed problem complicated by scene geometry (e.g., landscape, figures, objects within the image) and material properties (e.g., albedo). These problems become even more exacerbated with outdoor scenes because of factors that cannot be controlled such as ambient lighting and atmospheric turbidity. Conventional systems attempt to solve these problems in several ways but each have various drawbacks.
For example, some conventional systems rely on extracting cues by detecting shadows and/or shading within an image. These conventional systems estimate lighting conditions of an image by performing expensive calculations associated with the shadows within the image. While these conventional systems can recover image lighting conditions with reasonable estimates of scene geometry in simple images, these conventional systems return poor results when analyzing real-world images with less predictable light sources—e.g., where overlapping shadows, a blurred or weak shadow, or else no shadow at all. Additionally, these conventional systems require taxing, time-intensive calculations.
Other conventional systems estimate low-frequency lighting conditions but rely on user input to define baseline parameters such as image geometry and material properties. These estimates are not readily available or easy to compute in most cases. Therefore, these conventional systems may produce accurate image lighting condition estimations for specific user-controlled images, but these conventional systems are incapable of accurately adapting to changing environments. For example, light sources, such as the sun, can vary in intensity depending on various factors, which causes such conventional systems to return inaccurate lighting condition estimations.